Machine learning of colloquial place names

ABSTRACT

Provided are systems and methods directed to identifying relationships between colloquial place names in a relational database. In some embodiments, a method of identifying relationships between colloquial place names in a relational database comprises receiving geographic location information; generating a vector corresponding to the geographic location; comparing the geographic location information vector to a plurality of colloquial place name vectors in a relational database that maps a plurality of colloquial place names to a plurality of corresponding colloquial place name vectors in a vector space, to generate a plurality of similarity scores that is calculated based on the geographic location information vector and each colloquial place name vector of the plurality of colloquial place name vectors; and identifying that one or more colloquial place names in the relational database are related to the geographic location information based on the plurality of similarity scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.16/146,298, filed Sep. 28, 2018, the entire contents of which areincorporated herein by reference.

FIELD OF THE DISCLOSURE

This disclosure relates generally to machine learning of geographicplace names, and, particularly, to machine learning to associate relatedcolloquial geographic place names.

BACKGROUND OF THE DISCLOSURE

As access to geographic location information has increased in recentyears with improved global positioning system (GPS), location services,and other location identification technologies, so too has the varietyof applications and uses for this geographic information. Governments,law enforcement, companies, and researchers have all found ways toharness this location information to better serve their interests.However, this location data is all based on information that does nottake into account user input. For example, some social media platformsallow users to input their location when publishing a post by choosing apre-loaded location, tagging the post, and/or textually providinglocation information. The above-described location data does notconsider this user-inputted location information.

However, users often use unofficial descriptors when identifying theirlocation. For example, a user may refer to New York City using numerousdifferent colloquial terms such as New York, N.Y.; The Big Apple: NYC;New York; The City That Never Sleeps; etc. Colloquial terms that a usermay use to indicate that he is located in New York City include NewYorker; Yankee; and Knickerbocker.

Currently, there are systems available that can harvest thisuser-inputted colloquial geographic information by sorting andassociating like-terms together. For example, such systems maycategorically sort and associate terms such as “New Yorker”,“Bostonian”, “Washingtonian”, and “Chicagoan”. Other terms that may begrouped together include “NYC”, “BOS”, “DC”, and “CHI”. In bothexamples, the terms all categorically refer to their respective city ina similar way.

SUMMARY OF THE DISCLOSURE

Provided are systems, methods, and techniques for learning, grouping,interpreting, and suggesting colloquial place names. The providedsystems, methods, and techniques can be used to identify a geographiclocation of a user based on that user's input of a colloquial placename. Unlike methods that use GPS, location services, or other locationidentification technologies, the methods provided herein do not utilizegeographic-based data. The methods provided use colloquial terminologythat a user has chosen to associate with a geographic location. Methodsprovided also categorize the various colloquial names based on anofficial (i.e., non-colloquial) geographic location (e.g., New YorkCity, Washington, D.C., Chicago, etc.) instead of categorizing thecolloquial names categorically based on the terminology (as in theexample described above). Accordingly, provided herein are methods thatmay be used to search, map, or otherwise interpret noisy colloquialplace names.

Proper handling (e.g., learning, grouping, interpreting, and suggesting)of place names is important for at least mapping and identity-matchingtechniques. In some embodiments, methods provided herein may improve theproper handling required in such applications. Other examples that canbenefit from a more accurate method of handling place names includegeographic searching of social media users and cognitive assists (i.e.,Apple Inc.'s Siri). Methods provided herein may improve the handling ofcolloquial place names and allow other applications to benefit from theimproved handling.

A relational database can be developed based on a longitudinal samplingof users' social media metadata (e.g., colloquial place names). Methodsprovided herein can develop the relational database by applying analgorithm to the colloquial place names (for example, a word-embeddingalgorithm) to assign a vector to each unique colloquial place name.Vectors can be assigned to colloquial place names based on similarity toone another. For example, as described above, a relational database mayinclude vectors in a vector space that are associated with colloquialplace names such that a vector for “The Big Apple” and a vector for“NYC” are “closer” to each other in the vector space than the vector for“NYC” and a vector for “Beantown”.

Methods according to embodiments provided herein can be used for variousapplications. For example, one can query the relational database toidentify colloquial place names associated with a given geographiclocation. Or, one may have a colloquial place name, e.g., Beantown, anduse the learned database to identify other colloquial place names thatare similar.

In some embodiments, a database of colloquial place names may beprovided. The database may be populated with historical data based onvarious terms different people use to refer to themselves based on theirgeographic location. In some embodiments, this database may learn how tointerpret and group the colloquial place names based on geographiclocation. Once the database learns colloquial place names, a user mayuse the database to identify similar colloquial place names to a giventerm.

Methods provided herein may be used in various applications. Forexample, relational databases may be used to identify colloquial placenames associated with a particular geographic location, which may inturn be used to identify geographic trends, data, etc. Another use ofmethods provided herein may include geotagging. Geotagged online socialnetwork data may include photographs and/or videos, for example. Methodsprovided herein may be used to identify colloquial place namesassociated with a particular geographic location, which may in turn beused to identify specific geotagged online social network datacorresponding to the particular geographic location.

In some embodiments, a method of identifying relationships betweencolloquial place names in a relational database is provided, the methodcomprising: receiving geographic location information; generating ageographic location information vector corresponding to the geographiclocation; comparing the geographic location information vectorcorresponding to the geographic location to a plurality of colloquialplace name vectors in a relational database that maps a plurality ofcolloquial place names to a plurality of corresponding colloquial placename vectors in a vector space, wherein each colloquial place namevector represents one or more words associated with each colloquialplace name, to generate a plurality of similarity scores that iscalculated based on the geographic location information vector and eachcolloquial place name vector of the plurality of colloquial place namevectors; and identifying that one or more colloquial place names in therelational database are related to the geographic location informationbased on the plurality of similarity scores.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, the method comprisesoutputting the one or more colloquial place names related to thegeographic location information based on the similarity score onto adisplay.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, the method comprisesstoring the similarity score calculated based on the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vectors in the relational database.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, the method comprisesupdating the relational database based on the geographic locationinformation and the similarity score between the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vector.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, each colloquial placename vector of the plurality of colloquial place name vectors isgenerated by word-embedding one or more words associated with acolloquial place name of the plurality of colloquial place names,wherein a first colloquial place name vector represents a firstcolloquial place name and a second colloquial place name vectorrepresents a second colloquial place name.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, the similarity score iscalculated between the geographic location information vector and eachcolloquial place name vector by calculating a cosine similarity betweenthe geographic location information vector and each colloquial placename vector located in the vector space.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, for each user accountof a plurality of user accounts, receiving a plurality of colloquialplace names associated with each user account; and inputting theplurality of colloquial place names associated with each user accountinto a word-embedding algorithm to generate a mapping of the pluralityof colloquial place names to the plurality of corresponding colloquialplace name vectors, wherein the plurality of colloquial place namevectors corresponds to the plurality of colloquial place names.

In some embodiments of the method of identifying relationships betweencolloquial place names in a relational database, the word-embeddingalgorithm comprises one of word2vec, GloVe, or FastText.

In some embodiments, a method of generating a relational database ofcolloquial place names is provided, the method comprising receivingmetadata comprising a plurality of colloquial place names, wherein afirst plurality of colloquial place names of the plurality of colloquialplace names corresponds to a first user and a second plurality ofcolloquial place names of the plurality of colloquial place namescorresponds to a second user, and wherein each colloquial place name ofthe plurality of colloquial place names is associated with one or morewords; concatenating the one or more words associated with eachcolloquial place name of the first plurality of colloquial place namesto generate a first sequence corresponding to the first user;concatenating the one or more words associated with each colloquialplace name of the second plurality of colloquial place names to generatea second sequence corresponding to the second user; applying aword-embedding algorithm to the first sequence and the second sequenceto generate a vector space comprising a vector corresponding to each ofthe one or more words associated with each colloquial place name of thefirst plurality of colloquial place names and a vector corresponding toeach of the one or more words associated with each colloquial place nameof the second plurality of colloquial place names, wherein a firstvector corresponding to a first colloquial place name of the firstplurality of colloquial place names is located a first distance from asecond vector corresponding to a second colloquial place name of thefirst plurality of colloquial place names in the vector space, and thefirst vector is located a second distance from a third vectorcorresponding to a third colloquial place name of the second pluralityof colloquial place names, wherein the second distance is greater thanthe first distance; and storing the vector space into a relationaldatabase of colloquial place names.

In some embodiments of the method of generating a relational database ofcolloquial place names, the third vector corresponding to a thirdcolloquial place name of the second plurality of colloquial place namesis located a third distance from a fourth vector corresponding to afourth colloquial place name of the second plurality of colloquial placenames in the vector space, and the third vector is located a fourthdistance from the second vector corresponding to a second colloquialplace name of the first plurality of colloquial place names in thevector space, wherein the fourth distance is greater than the thirddistance.

In some embodiments of the method of generating a relational database ofcolloquial place names, the first distance is determined by calculatinga Euclidean distance between the first vector corresponding to the firstcolloquial place name of the first plurality of colloquial place namesand the second vector corresponding to the second colloquial place nameof the first plurality of colloquial place names and the second distanceis determined by calculating a Euclidean distance between the firstvector and the third vector corresponding to a third colloquial placename of the second plurality of colloquial place names.

In some embodiments of the method of generating a relational database ofcolloquial place names, the third distance is determined by calculatinga Euclidean distance between the third vector corresponding to the thirdcolloquial place name of the second plurality of colloquial place namesand the fourth vector corresponding to the fourth colloquial place nameof the second plurality of colloquial place names and the fourthdistance is determined by calculating a Euclidean distance between thethird vector and the second vector corresponding to a second colloquialplace name of the first plurality of colloquial place names in thevector space.

In some embodiments of the method of generating a relational database ofcolloquial place names, the word-embedding algorithm comprises one ofword2vec, GloVe, or FastText.

In some embodiments of the method of generating a relational database ofcolloquial place names, the metadata comprises a plurality of colloquialplace names associated with a plurality of users of a social mediaplatform, and wherein two or more colloquial place names of theplurality of colloquial place names are associated with each user of theplurality of users.

In some embodiments of the method of generating a relational database ofcolloquial place names, each colloquial place name of the firstplurality of colloquial place names corresponding to the first usercorrespond to a first geographic location and each colloquial place nameof the second plurality of colloquial place names corresponding to thesecond user correspond to a second geographic location.

In some embodiments of the method of generating a relational database ofcolloquial place names, the one or more words associated with eachcolloquial place names of the plurality of colloquial place namescomprises one or more of a letter of an alphabet, a numeral, a symbol,punctuation, or an emoji.

In some embodiments of the method of generating a relational database ofcolloquial place names, the method comprises applying a de-duplicationalgorithm to the first sequence and the second sequence to eliminatefrom the metadata any duplicate colloquial place names associated with asingle user; applying a tokenizing algorithm to the first sequence andthe second sequence to identify and insert boundaries into the one ormore words associated with each colloquial place name; and applying aphrase-identifying algorithm to the first sequence and the secondsequence to identify one or more words that comprise a multi-wordphrase.

In some embodiments, a system for identifying relationships betweencolloquial place names in a relational database is provided, the systemcomprising one or more processors and memory storing one or moreprograms that when executed by the one or more processors cause the oneor more processors to: receive geographic location information;generate, based on the geographic location information, a vectorcorresponding to the geographic location; compare the geographiclocation information vector corresponding to the geographic location toa plurality of colloquial place name vectors in a relational databasethat maps a plurality of colloquial place names to a plurality ofcorresponding colloquial place name vectors in a vector space, whereineach colloquial place name vector represents one or more wordsassociated with each colloquial place name, to generate a plurality ofsimilarity scores that is calculated based on the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vectors; and identify that one ormore colloquial place names in the relational database are related tothe geographic location information based on the plurality of similarityscores.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the system comprisesoutput the one or more colloquial place names related to the geographiclocation information based on the similarity score onto a display.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the system comprisesstore the similarity score calculated based on the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vectors in the relational database.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the system comprisesupdate the relational database based on the geographic locationinformation and the similarity score between the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vector.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, each colloquial placename vector of the plurality of colloquial place name vectors isgenerated by word-embedding one or more words associated with acolloquial place name of the plurality of colloquial place names,wherein a first colloquial place name vector represents a firstcolloquial place name and a second colloquial place name vectorrepresents a second colloquial place name.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the system comprisesthe similarity score is calculated between the geographic locationinformation vector and each colloquial place name vector by calculatinga cosine similarity between the geographic location information vectorand each colloquial place name vector located in the vector space.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the system comprisesfor each user account of a plurality of user accounts, receive aplurality of colloquial place names associated with each user account;and input the plurality of colloquial place names associated with eachuser account into a word-embedding algorithm to generate a mapping ofthe plurality of colloquial place names to the plurality ofcorresponding colloquial place name vectors, wherein the plurality ofcolloquial place name vectors corresponds to the plurality of colloquialplace names.

In some embodiments of the system for identifying relationships betweencolloquial place names in a relational database, the word-embeddingalgorithm comprises one of word2vec, GloVe, or FastText.

In some embodiments, a system for generating a relational database ofcolloquial place names is provided, the system comprising: one or moreprocessors and memory storing one or more programs that when executed bythe one or more processors cause the one or more processors to: receivemetadata comprising a plurality of colloquial place names, wherein afirst plurality of colloquial place names of the plurality of colloquialplace names corresponds to a first user and a second plurality ofcolloquial place names of the plurality of colloquial place namescorresponds to a second user, and wherein each colloquial place name ofthe plurality of colloquial place names is associated with one or morewords; concatenate the one or more words associated with each colloquialplace name of the first plurality of colloquial place names to generatea first sequence corresponding to the first user; concatenate the one ormore words associated with each colloquial place name of the secondplurality of colloquial place names to generate a second sequencecorresponding to the second user; apply a word-embedding algorithm tothe first sequence and the second sequence to generate a vector spacecomprising a vector corresponding to each of the one or more wordsassociated with each colloquial place name of the first plurality ofcolloquial place names and a vector corresponding to each of the one ormore words associated with each colloquial place name of the secondplurality of colloquial place names, wherein a first vectorcorresponding to a first colloquial place name of the first plurality ofcolloquial place names is located a first distance from a second vectorcorresponding to a second colloquial place name of the first pluralityof colloquial place names in the vector space, and the first vector islocated a second distance from a third vector corresponding to a thirdcolloquial place name of the second plurality of colloquial place names,wherein the second distance is greater than the first distance; andstore the vector space into a relational database of colloquial placenames.

In some embodiments of the system for generating a relational databaseof colloquial place names, the third vector corresponding to a thirdcolloquial place name of the second plurality of colloquial place namesis located a third distance from a fourth vector corresponding to afourth colloquial place name of the second plurality of colloquial placenames in the vector space, and the third vector is located a fourthdistance from the second vector corresponding to a second colloquialplace name of the first plurality of colloquial place names in thevector space, wherein the fourth distance is greater than the thirddistance.

In some embodiments of the system for generating a relational databaseof colloquial place names, the first distance is determined bycalculating a Euclidean distance between the first vector correspondingto the first colloquial place name of the first plurality of colloquialplace names and the second vector corresponding to the second colloquialplace name of the first plurality of colloquial place names and thesecond distance is determined by calculating a Euclidean distancebetween the first vector and the third vector corresponding to a thirdcolloquial place name of the second plurality of colloquial place names.

In some embodiments of the system for generating a relational databaseof colloquial place names, the third distance is determined bycalculating a Euclidean distance between the third vector correspondingto the third colloquial place name of the second plurality of colloquialplace names and the fourth vector corresponding to the fourth colloquialplace name of the second plurality of colloquial place names and thefourth distance is determined by calculating a Euclidean distancebetween the third vector and the second vector corresponding to a secondcolloquial place name of the first plurality of colloquial place namesin the vector space.

In some embodiments of the system for generating a relational databaseof colloquial place names, the word-embedding algorithm comprises one ofword2vec, GloVe, or FastText.

In some embodiments of the system for generating a relational databaseof colloquial place names, the metadata comprises a plurality ofcolloquial place names associated with a plurality of users of a socialmedia platform, and wherein two or more colloquial place names of theplurality of colloquial place names are associated with each user of theplurality of users.

In some embodiments of the system for generating a relational databaseof colloquial place names, each colloquial place name of the firstplurality of colloquial place names corresponding to the first usercorrespond to a first geographic location and each colloquial place nameof the second plurality of colloquial place names corresponding to thesecond user correspond to a second geographic location.

In some embodiments of the system for generating a relational databaseof colloquial place names, the one or more words associated with eachcolloquial place names of the plurality of colloquial place namescomprises one or more of a letter of an alphabet, a numeral, a symbol,punctuation, or an emoji.

In some embodiments of the system for generating a relational databaseof colloquial place names, the system comprises apply a de-duplicationalgorithm to the first sequence and the second sequence to eliminatefrom the metadata any duplicate colloquial place names associated with asingle user; apply a tokenizing algorithm to the first sequence and thesecond sequence to identify and insert boundaries into the one or morewords associated with each colloquial place name; and apply aphrase-identifying algorithm to the first sequence and the secondsequence to identify one or more words that comprise a multi-wordphrase.

BRIEF DESCRIPTION OF THE FIGURES

The invention will now be described, by way of example only, withreference to the accompanying drawings, in which:

FIG. 1 illustrates a system for identifying colloquial place names,according to some embodiments;

FIG. 2 illustrates a diagram showing how an example colloquial placename relational database may be generated, according to someembodiments;

FIG. 3 illustrated a diagram showing how example relationships may begenerated between colloquial place names in a relational database,according to some embodiments;

FIG. 4 illustrates a diagram showing how one or more colloquial placename can be selected based on an input value, according to someembodiments;

FIG. 5 illustrates a method for generating relationships betweencolloquial place names and identifying related colloquial place names ina relational database to geographic information, according to someembodiments;

FIG. 6 illustrates a method for generating a relational database ofcolloquial place names, according to some embodiments; and

FIG. 7 illustrates an example of a computing device according to someembodiments.

DETAILED DESCRIPTION OF THE DISCLOSURE

Described are exemplary embodiments of systems, methods, and techniquesfor developing relational databases of colloquial place names. Theserelational databases may then be used to interpret and suggestcolloquial place names based on various input values. Many currentrelational databases including location information are based onofficial geographic locations instead of colloquial place names. Somecurrent relational databases including location information that arebased on colloquial place names associate two colloquial place namescategorically instead of geographically. (For example,categorically-grouped colloquial place name relational databases may beused to predict that “Michiganer” is more similar to “Californian” (bothterms describe a person from a state) than it is to “Mitten State” (aterm identifying a state). Conversely, geographically-grouped colloquialplace names according to embodiments herein can be used to predict that“Michiganer” is more similar to “Mitten State” (both terms are relatedto the same state) than it is to “Californian.”) Embodiments describedherein are directed to developing a relational database of colloquialplace names mapped to vectors that are generated based on geographiclocation. By associating colloquial place names with each other based oninput data, a relational database developed according to embodimentsdescribed herein may be able to interpret a colloquial place name,identify similar colloquial place names based on geographic location,and/or colloquial place names based on an input value.

As described below, a longitudinal sampling of metadata can be obtainedfrom social media platforms. The metadata can comprise geographiclocation information associated with a plurality of users over a periodof time. Some social media platforms permit users to freely inputgeographic location information instead of identifying a geographiclocation provided in a populated list. For example, on some social mediaplatforms, users can input any string of letters, numerals, punctuation,and/or emojis into a field associated with their user account or userprofile. Accordingly, many users choose to input nicknames, orcolloquial place names, to identify a geographic location. A user maychoose to provide “The Mile High City” or “DEN” (colloquial place namesassociated with Denver, Colo.) instead of identifying “Denver, Colo.”specifically. This presents challenges for anyone wishing to identifyrelationships between geographically-related colloquial place names.

However, it has been determined that the various colloquial place namesassociated with a single user are related in some meaningful way. Forexample, a user may move to various different towns or neighborhoods allwithin a greater geographic region. Another user may change thecolloquial place name used to identify a location as he ages (i.e.,progresses from high school, to college, and into adulthood). In somecases, a user may move from one geographic location to a different, yetsimilar, geographic location. For example, a user may move from Boston,Mass. to Philadelphia, Pa. or from Des Moines, Iowa to Lincoln, Nebr. Insome embodiments, new colloquialisms may become associated with ageographic location over time due to changes in popular culture. Forexample, a user may change his or her stated place name from “Cleveland”to “Believeland” in response to a local sports team's success. Thus, byusing these relationships between colloquial place names associated witheach individual user, relational databases can be developed, describedin detail below. A relational database based on these relationshipsbetween colloquial place names can then be used to identifyrelationships between given geographic location information and thecolloquial place names provided in the relational database, alsoprovided below.

Methods for developing a relational database including colloquial placenames can include automatically learning from input data a similaritybetween different terms. For example, people can refer to Chicago as“Chi-town” or “The Windy City” and may refer to a person from Chicago asa “Chicagoan”. Systems according to embodiments provided herein canlearn that the terms Chicago, Chi-town, The Windy City, and Chicagoanare all referring to contextually similar geographic locations—as wellas a non-colloquial term such as Chicago, Ill.—and associate these termsby assigning vectors to each term accordingly.

In some embodiments, a system can be configured to identify one or morecolloquial place names associated with a geographic location using arelational database (e.g., the colloquial place name “Beantown” may beassociated with the geographic location “Boston, Mass.”). In someembodiments, a system can be configured to identify one or morecolloquial places name similar to a colloquial place name provided by auser using a relational database (e.g., a user may input “DEN” and thecolloquial place names “Denver”, “Mile High City”, and “5280” may beidentified as similar to “DEN”). These identification functions can beperformed with high accuracy by using a relational database that embedsa plurality of colloquial place names into a vector space wherein eachcolloquial place name is embedded as a corresponding positional vectorin a vector space. To embed the plurality of names, a system can beconfigured to implement word-embedding in which related colloquial placenames are embedded as “closer” (or “nearby”) positional vectors (e.g., arepresentation for a point) in the vector space. “Closer” positionalvectors can be quantified by a distance metric (e.g., a Euclideandistance, a Pearson correlation, a Minkowski distance, etc.) such that afirst positional vector can be closer to a second positional vector ifthe distance between the first and second positional vectors is lessthan that between the first and a third positional vector.

In some embodiments, the embedded colloquial place names can be storedin the relational database as associations between a plurality ofcolloquial place names and a plurality of corresponding colloquial placename vectors, wherein each colloquial place name vector can be apositional vector in the vector space and associated with one or morewords associated with each colloquial place name.

FIG. 1 illustrates a system 100 for selecting one or more colloquialplace names that correspond to an input value, according to someembodiments. System 100 includes a data-processing device 102communicatively coupled to a colloquial place name relational database120. In some embodiments, data-processing device 102 can be implementedon one or more virtual machines, servers, hardware appliance,general-purpose computers, or a combination thereof. In someembodiments, data-processing device 102 can be communicatively coupledto a user client such as a mobile device or a personal laptop to enableusers to access the colloquial place name identifying functionalityprovided by data-processing device 102. In some embodiments,data-processing device 102 can be coupled to the client device via anetwork that includes a local area network (LAN), a wide area network(WAN), the Internet, a Wi-Fi network, a WiMAX network, a cellularnetwork (e.g., 3G, 4G, 4G Long Term Evolution (LTE)), or a combinationthereof. Further, the network may implement one or more wired and/orwireless standards or protocols.

In some embodiments, colloquial place name relational database 120 canbe configured to store associations between a plurality of colloquialplace names 122 and a plurality of corresponding colloquial place namevectors 124. In some embodiments, colloquial place name relationaldatabase 120 can be configured to store the associations in a database,such as a relational database that stores associations in one or moretables. In some embodiments, data-processing device 102 can beconfigured to embed the plurality of colloquial place names 122 into avector space including the plurality of colloquial place name vectors124. The embedding process includes converting a colloquial place nameinto a positional vector in the vector space, in which the positionalvector corresponds to the colloquial place name vector and can be ann-dimensional vector where n corresponds to a number of elements in thecolloquial place name vector. The colloquial place name vector can be abinary vector in which each element is a binary value (e.g., 0 or 1), aninteger vector in which each element is an integer, or a real-valuedvector in which each element can be represented by, e.g., a double datatype.

In some embodiments, data-processing device 102 can be configured toperform the colloquial place name embedding process using characterembedding, n-gram embedding, or word-embedding. In some embodiments,character embedding includes associating each character of a colloquialplace name with a vector and generating a colloquial place name vectorhaving a fixed width (e.g., being n elements) by inputting each of thevectors into a neural network (e.g., a recurrent neural network). Insome embodiments, n-gram embedding includes projecting a colloquialplace name onto a vector based on unigrams (one character), bigrams (twocontiguous characters), n-grams (n contiguous characters), or acombination thereof in the name. Then, the resulting vector of then-gram embedding can be compressed to a fixed-size colloquial place namevector having n elements. For example, the resulting vector may behashed into the fixed-size colloquial place name vector. In someembodiments, as will be further described with respect to FIG. 2 ,word-embedding includes embedding related place names as “closer”positional vectors in a vector space.

In some embodiments, data-processing device 102 can be configured toimplement word-embedding to embed colloquial place names 122 into avector space including a plurality of colloquial place name vectors 124where each of colloquial place names 122 can be stored in associationwith a unique colloquial place name vector of real numbers. For example,a colloquial place name and a corresponding colloquial place name vectormay be stored in the same entry of a table in the relational database.In some embodiments, the colloquial place name vector can be ann-dimensional vector where n corresponds to a number of elements in eachname vector. In some embodiments, the colloquial place name vector usedto represent a colloquial place name can be associated with a pluralityof words associated with that colloquial place name, which is furtherdescribed below with respect to FIG. 2 .

In some embodiments, colloquial place name embedder 104 can beconfigured to generate colloquial place name relational database 120 byword-embedding a word or a plurality of words associated with eachcolloquial place name into a vector space used to represent colloquialplace names. For example, each colloquial place name may be convertedinto a colloquial place name vector in the vector space where thecolloquial place name vector includes a plurality of real values. Insome embodiments, colloquial place name embedder 104 receivesinformation related to user accounts of social media to performword-embedding. For example, colloquial place name embedder 104 mayperform word-embedding on a plurality of metadata associated with eachuser account, as will be further described below with respect to FIG. 2. In some embodiments, colloquial place name embedder 104 can beconfigured to run a word-embedding algorithm such as Word2Vec or GloVe.

In some embodiments, selection processor 112 can be configured to selectone or more colloquial place name from a plurality of colloquial placenames 122 based on input value 106 received at data-processing device102. For example, input value 106 may include a non-colloquialgeographic location or a colloquial place name. Using this input value,the relational database may be used to select one or more colloquialplace names associated with input value 106. In some embodiments, theone or more selected colloquial place names may identify nicknames,words, phrases, or another string of text. In some embodiments, acolloquial place name may include letters, numbers, punctuation,symbols, and/or emojis.

In some embodiments, upon receiving input value 106, data-processingdevice 102 can be configured to generate an input value vectorcorresponding to the input value. Data-processing device 102 can then beconfigured to calculate a plurality of similarity scores between theinput value vector and a plurality of colloquial place name vectorscorresponding to the plurality of colloquial place names. Eachsimilarity score may quantify a similarity between the input valuevector and one or more colloquial place name vectors. In someembodiments, a higher similarity score may indicate greater similarityin which case a colloquial place name vector having the highest scorefrom the plurality of similarity scores and associated with a colloquialplace name may be determined to be similar to the input value. In someembodiments, a high similarity score corresponds to a shorter distancecalculated between vectors, in which case a colloquial place name vectorbeing selected is predicted to correspond to the input value and may beassociated with the shortest distance to the input value vector ascompared to the distance between the input value vector and each of theother colloquial place name vectors from the plurality of colloquialplace name vectors.

In some embodiments, colloquial place name-vector selector 110 can beconfigured to select a colloquial place name vector based on acolloquial place name by searching name relational database 120. Forexample, when name relational database 120 is configured as a databasethat stores mappings between colloquial place names 122 andcorresponding colloquial place name vectors 124, colloquial placename-vector selector 110 may query the database for the colloquial placename vector associated with the colloquial place name. As describedabove, the selected colloquial place name vector includesrepresentations (e.g., real numbers) associated with one or more wordsor characters (e.g., colloquial place names) associated with thecolloquial place name.

FIG. 2 illustrates a diagram 200 showing the generation of an examplecolloquial place name relational database 206 according to someembodiments. Word embedder 204 (e.g., colloquial place name embedder 104of FIG. 1 ) can be configured to apply a word-embedding algorithm toembed colloquial place names in input relational database 202 intovector representations, as shown in colloquial place name relationaldatabase 206. For example, the word-embedding algorithm may includeWord2Vec, GloVe, FastText, or any other suitable word-embeddingalgorithm. In general, word-embedding algorithms such as Word2Vec andGloVe can implement neural networks to generate fixed-dimensional vectorsummaries of word traits such as syntactic and semantic categories basedon a large set of documents. These neural networks may be configured tooptimize the ability of a generated vector for a word to correctlypredict language phenomena within the local area of occurrence (e.g.,within a sentence or a window of a fixed number of words) of that word.As further described below, input relational database 202 can beconfigured to include sets of colloquial place names associated with agiven geographic location.

By taking advantage of the optimization functionality of word-embeddingalgorithms, word embedder 204 may be configured to word embed associatedcolloquial place names as corresponding colloquial place name vectorsthat are “close” to each other in the colloquial place name vector spaceaccording to some embodiments. In particular, a first plurality of words(e.g., a plurality of colloquial place names associated with a firstuser account) may all be related to a first geographic location (e.g.,Boston, Mass.). A second plurality of words (e.g., a second plurality ofcolloquial place names associated with a second user account) may all berelated to a second geographic location (e.g., Chicago, Ill.). A thirdplurality of words (e.g., a third plurality of colloquial place namesassociated with a third user account) may all be related to a thirdgeographic location (e.g., New York, N.Y.). A fourth plurality of words(e.g., a fourth plurality of colloquial place name associated with afourth user account) may all be related to a fourth geographic location(e.g., Washington, D.C.).

An implicit assumption is that one or more words corresponding to one ormore colloquial place name and associated with any given single useraccount are related to one another in some meaningful way. For example,a user may move to various different towns or neighborhoods all within agreater geographic region. Another user may change the colloquial placename used to identify a location as he ages (i.e., progresses from highschool, to college, and into adulthood). In some cases, a user may movefrom one geographic location to a different, yet similar, geographiclocation. For example, a user may move from Boston, Mass. toPhiladelphia, Pa. or from Des Moines, Iowa to Lincoln, Nebr. Thesimilarities between the various colloquial place names and theircorresponding geographic locations in the examples above can all providemeaningful information that can be incorporated by methods provided toaccount for similarities and differences between various colloquialplace names.

In some cases, one or more words corresponding to two or more relatedcolloquial place names may be associated with a single user account. Forexample, a user may move from Boston, Mass. to Des Moines, Iowa, whichare two different places with little in common geographically. However,this may only be a single data point in the entire input data. Otheruser accounts from the longitudinal sample may include colloquial placenames associated with Boston, Mass. Similarly, other user accounts inthe input data from the longitudinal sample may include colloquial placenames associated with Des Moines, Iowa. The word-embedding algorithm maybe able to generate a Beantown vector that is “closer” to a BOS vectorin a vector space than it is to a Des Moines vector, even though thecolloquial place names “Beantown” and “Des Moines” are associated withthe same user account. However, the word-embedding algorithm may accountfor the association of these colloquial place names with other useraccounts as well. For example, the colloquial place name “Beantown” mayalso be associated with a user account that also includes the colloquialplace names of “Fenway”, “617”, and “Bostonian”. Similarly, thecolloquial place name “Des Moines” may also be associated with a useraccount that includes the colloquial place names of “IA”, “The HawkeyeState”, and “DSM”, all nicknames or abbreviations referring to DesMoines or Iowa generally. Accordingly, while the word-embeddingalgorithm will consider the relationship between “Beantown” and “DesMoines” based on their common association with a single user account,the word-embedding algorithm will also account for their presence in andassociation with other user accounts. In many cases, the presence ofcolloquial place names “Beantown” and “Des Moines” in other useraccounts including more geographically-related colloquial place nameswill outweigh a single user account including both “Beantown” and “DesMoines”, and thus, the word-embedding algorithm can generate a Beantownvector that is “closer” to “Fenway”, “617”, and “Bostonian” vectors, anda “Des Moines” vector that is “closer” to “IA”, “The Hawkeye State”, and“DSM” vectors in a vector space.

Similarly, a user account may include the colloquial place names:“Philly”, “Chinatown”, “Main Line”, and “PA”. However, colloquial placenames such as “Chinatown” may have little more relation to Philadelphiathan it does to any other city having a Chinatown neighborhood.Accordingly, the colloquial place name “Chinatown” would likely bepresent in the metadata of user accounts associated with a large varietyof geographic locations. However, like the example provided above, aword-embedding algorithm can account for the associations between“Chinatown” and a plurality of various colloquial place namescorresponding to different cities by generating vectors for “Chinatown”and the plurality of various colloquial place names accordingly.

Upon performing word-embedding on the words in each of the firstplurality of words, second plurality of words, third plurality of words,fourth plurality of words, etc., colloquial place name embedder 204 maygenerate a first plurality of colloquial place name vectors for allunique words and/or phrases from relational database 202 of which thevectors corresponding to the first plurality of colloquial place namevectors will be closer to each other than, for example, vectors betweenthe first and second plurality of place names. For example, vectors for“Beantown” and “617” (both from the first plurality of colloquial placenames) will be closer to each other than vectors for “Beantown” (fromthe first plurality) and “Yankee” (from the third plurality).

In some embodiments, input relational database 202 includes alongitudinal sample of a plurality of metadata from user accounts of asocial media platform. This metadata can include various wordsassociated with each particular user account at various points in time.In some embodiments, the words associated with each user account caninclude colloquial place names. Further, the “words” may includeletters, punctuation, symbols, emoji, and/or other characters. Forexample, input relational database 202 may include for user 10101 thefollowing colloquial place names: Beantown, 617, BOS, Boston, Chinatown,and Fenway (617 referring to the local area code). In another example,input relational database 202 may include for user 10104 the followingcolloquial place names: DC, Washington, Washington D.C., Arlington, andDMV.

In some embodiments, input relational database 202 includes a pluralityof metadata associated with a plurality of users and the metadata caninclude a plurality of words associated with each user. Each of thewords may be a colloquial place name associated with that user. Forexample, as shown in the examples provided above, the words may refer toa user's city of residence or hometown, a common abbreviation referringto a geographic region associated with a user (e.g., DMV which standsfor “DC-Maryland-Virginia”), an area code or zip code of a user, anairport code (e.g., BOS), nicknames for a geographic area associatedwith a user (e.g., Beantown, Wash.), and/or a neighborhood within aspecific geographic location (e.g., Chinatown, Fenway). In someembodiments, the colloquial place names associated with a user may beobtained by a longitudinal sampling of the metadata of social mediaplatforms. In particular, some social media platforms allow users tomanually input their location (as opposed to selecting a location from apopulated list). Social media platforms allowing users to manually inputtheir location can provide data for input relational database 202. Notethat the precise geographic location of the user may be irrelevant.Methods provided are based on relationships between a given user'smetadata (i.e., changes in colloquial place names over a period of time)which may or may not accurately portray a user's geographic location.For example, a user may choose to provide a colloquial place nameassociated with his hometown or the town of his alma mater, which maynot accurately represent his geographic location at a given point intime.

In some embodiments, word embedder 204 can be configured to concatenateeach plurality of words associated with each user account to generate aplurality of sequences corresponding to the plurality of user accounts.Then, word embedder 204 can be configured to apply the word-embeddingalgorithm on the plurality of sequences to generate colloquial placename relational database 206. As discussed above with respect to FIG. 1, word-embedding converts each colloquial place name from inputrelational database 202 into a vector representation in a vector spaceof n-dimensions. Each of the vector representations may be a positionalvector (e.g., a representation of a point in the vector space) having nelements. In some embodiments, word-embedding embeds related colloquialplace names into “closer” positional vectors in the vector space. Forexample, the positional vectors corresponding to “DMV” and “Washington”may be closer to each other than the positional vectors correspondingto, for example, “DMV” and “Beantown.”

Before converting each colloquial place name from input relationaldatabase 202 into a vector representation as described above, variousfunctions can be performed on the plurality of sequences to clean-up orotherwise simplify the data. In particular, the plurality of sequencescan be processed through an algorithm to determine if any words maybelong together as a phrase. For example, Word2Phrase, pointwise mutualinformation, or other suitable collocation detection algorithms may beused to learn phrases from the plurality of sequences. Words such as“The”, “Windy”, and “City” may be recognized by Word2Phrase ascomprising a single colloquial place name—“The Windy City”. Byidentifying which of the input words show up next to each otherdisproportionately frequently, phrases such as “The Windy City” may beidentified in the plurality of sequences. Additionally, the plurality ofsequences can be passed through a de-duplication process (or bloomfilter) to identify and remove any duplications of user-colloquial placename pairings. For example, if all data points associated with aparticular user from the longitudinal sample include “Philly” as acolloquial place name and nothing else, all but one data point (i.e., auser account-colloquial place name pairing) will be eliminated, sinceany data point in excess of one for this particular user will be aduplicate. In some embodiments, if a particular user only has a singlecolloquial place name after a de-duplication filter has been run on thedata, that user may be eliminated from the data entirely. As discussedabove, the word-embedding process, wherein a vector is generated andassociated to each colloquial place name, is dependent upon changes in auser's colloquial place name. Thus, if a particular user has not changedhis or her colloquial place name over the course of the longitudinalsampling, there cannot be any meaningful associations generated fromthat user's data, since they only have a data point associated withtheir user account.

Another function that may be performed on the plurality of sequences isa tokenizing function. In some cases, a colloquial place name mayinclude punctuation, a symbol, or an emoji instead of a space. Atokenizing function can insert spaces where it identifies a wordboundary. For example, “Los˜Angeles” may be converted to “Los Angeles”and “The_Windy_City” to “The Windy City”. Once any of theabove-described and/or other similar functions have been performed onthe plurality of sequences, the plurality of sequences can be processedby a word-embedding algorithm. Some word-embedding algorithms mayinclude Word2Vec, GloVe, fastText, ELMo, Explicit Semantic Analysis(ESA), and other suitable algorithms.

Once a colloquial place name vector has been generated for eachcolloquial place name, the similarity between two different colloquialplace name vectors associated with two respective colloquial place namescan be calculated. As discussed above, the “closeness” between twovectors can be quantified by calculating a distance metric between thetwo vectors. For example, the distance metric may be calculated based ona Euclidean distance, a cosine distance, a Pearson correlation, aManhattan distance, a Minkowski distance, etc. In each case, a smallerdistance may indicate that two vectors are “closer” to each other in thevector space. For example, a first positional vector is closer to asecond positional vector than a third positional vector if the distancebetween the first and second positional vector is less than that betweenthe first and third positional vectors.

As shown in diagram 200, colloquial place name relational database 206embeds the colloquial place names from each plurality of colloquialplace names into a vector space where each colloquial place name can beassociated with a unique colloquial place name vector, as shown inrelational database 206. For example, the colloquial place name “ChiTown” is associated with the colloquial place name vector[0.78,0.13,0.79,0.55,0.68,0.79,0.27,0.62,0.84,0.38,0.05, . . . ].

FIG. 3 illustrates a diagram showing process 300 for comparing vectorsand generating a plurality of similarity scores according to someembodiments. Process 300 can include geographic location information308, a relational database 306 comprising a plurality of colloquialplace name vectors corresponding to a plurality of colloquial placenames, colloquial place name-vector selector 310 (e.g., colloquial placename-vector selector 110 of FIG. 1 ), and a plurality of similarityscores 336.

Geographic location information 310 may be an input provided by a user.For example, geographic location information 310 may include acolloquial place name, an official (non-colloquial) geographic location,or any other geographic identifier (e.g., latitude, longitude, cardinaldirections, etc.)

Relational database 306 (e.g., relational database 106 of FIG. 1 ) cancomprise a plurality of colloquial place names. The plurality ofcolloquial place names may correspond to one or more words. Aword-embedding algorithm can generate a vector corresponding to the oneor more words of each of colloquial place name of the plurality ofcolloquial place names and can map the colloquial place name vectors tothe colloquial place names in a vector space. This process is describedin more detail with respect to FIG. 2 , above.

The vector corresponding to geographic location information 308 can becompared to any or all of the colloquial place name vectors of theplurality of colloquial place name vectors corresponding to theplurality of colloquial place names. In some embodiments, the vectorcorresponding to geographic location information 308 can be compared bycalculating a similarity score between the vector corresponding togeographic location information 308 and any or all of the colloquialplace name vectors of the plurality of colloquial place name vectorscorresponding to the plurality of colloquial place names. A similarityscore can be calculated between the vector corresponding to geographiclocation information 308 and each of the colloquial place name vectorsof the plurality of colloquial place names corresponding to theplurality of colloquial place names to generate plurality of similarityscores 336. For example, a similarity score can be generated bycalculating a cosine similarity, a Euclidean distance, a Sorensen-Dicecoefficient, Jaccard index, or any other suitable method for calculatingthe similarity between two vectors.

Once a plurality of similarity scores 336 have been calculated, one ormore colloquial place names corresponding to one or more colloquialplace name vectors may be identified as being “similar” or “related to”the geographic information. For example, one or more colloquial placenames corresponding to the one or more colloquial place name vectorsabove a certain threshold value may be identified as “similar” to thegeographic information. In some embodiments, the identified one or morecolloquial place names can be outputted to a display and displayed to auser. In some embodiments, plurality of similarity scores 336 can bestored in the relational database. In some embodiments, the relationaldatabase can be updated based on geographic location information 308 andthe plurality of similarity scores 336.

FIG. 4 illustrates diagram 400 showing how a colloquial place name or aplurality of colloquial place names can be selected for a given inputvalue according to some embodiments. The following descriptions mayrefer to the components of data-processing device 102 and variousrelational databases 120, 130, and 140, as described above with respectto FIG. 1 .

Selection processor 430 (e.g., selection processor 112 of FIG. 1 ) canbe configured to select one or more of colloquial place names 420A andcolloquial place name 420B for a received input value 402. As shown indiagram 400, selection processor 430 may compare a input value vector416 corresponding to input value 402 with a plurality of colloquialplace name vectors 426A and 426B corresponding to colloquial place names420A and 420B to select one or more of colloquial place names 420A and420B as being associated with input value 402.

In some embodiments, colloquial place name-vector selector 406 (e.g.,colloquial place name-vector selector 110) can select one or more ofcolloquial place name vector 426A and 426B based on input value 402. Forexample, colloquial place name-vector selector 406 may query a database(e.g., colloquial place name relational database 120) storingassociations between colloquial place names and colloquial place namevectors to retrieve one or more of colloquial place name vector 426A and426B. In some embodiments, the associations between colloquial placenames and colloquial place name vectors can be generated based on aword-embedding process, as discussed above with respect to FIGS. 1 and 2. For example, word-embedding processes 422A and 422B can use Word2Vec,as shown in FIG. 4 , to generate colloquial place name vectors 426A and426B that correspond to colloquial place names 420A and 420B,respectively.

In some embodiments, selection processor 430 can be configured tocalculate a similarity score for each of colloquial place name vectors426A and 426B where each similarity score quantifies a similaritybetween each of colloquial place name vector 426A and 426B and inputvalue vector 416. In some embodiments, selection processor 430 can rankcolloquial place name vectors 426A and 426B by the correspondingsimilarity scores to determine one or more of corresponding colloquialplace names 420A and 420B that are more closely associated with receivedinput value 402. In some embodiments, calculating the similarity scorebetween two vectors includes calculating a distance metric (e.g., aEuclidean distance). In these embodiments, a smaller distance indicatesthat the two vectors are more “similar” and more closely associated witheach other. For example, selection processor 430 may calculate Euclideandistances of 15.45 and 12.79 for colloquial place name vectors 426A and426B, respectively. In this example, selection processor 430 may selectand output colloquial place name 420B corresponding to colloquial placename vector 426B as the colloquial place name of “The Mile High City.”According to process 400, this means that colloquial place name 420B, or“The Mile High City”, is more similar to input value 402 of “Denver”than colloquial place name 420A, or “Beantown”.

Input value 402 may include various categories of information. Forexample, input value 402 may include an official (non-colloquial)geographic location, a colloquial place name, a user identification,etc.

FIG. 5 illustrates a flowchart of a method 500 for identifyingrelationships between colloquial place names in a relational databaseaccording to some embodiments. Method 500 can be performed by adata-processing device such as data-processing device 102 of FIG. 1 .Accordingly, one or more of the steps below may reference the componentsof data-processing device 102. Method 500 can be performed by processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsrunning on a processing device), or a combination thereof.

In step 502, a selection processor (e.g., selection processor 112) ofthe data-processing device receives geographic location information. Forexample, the data-processing device may include a user interface thatallows a user to enter the colloquial place name on an input device(e.g., a keyboard or a touchscreen). In some embodiments, the selectionprocessor receives the geographic location information from a remotedevice (e.g., a mobile device or a server) operated by the user. Asmentioned above, geographic location information may include an official(non-colloquial) geographic location, a colloquial place name, etc.

In step 504, a vector can be generated to correspond to the geographiclocation information. For example, a word-embedding algorithm may beused to generate a geographic location information vector correspondingto the geographic location information.

In step 506, the geographic location information vector can be comparedto a plurality of colloquial place name vectors in a relationaldatabase. The plurality of colloquial place name vectors can correspondto a plurality of colloquial place names in a vector space of therelational database. To compare the geographic location informationvector to the plurality of colloquial place name vectors, a plurality ofsimilarity scores can be calculated. A similarity score can becalculated between the geographic location information vector and eachcolloquial place name vector of the plurality of colloquial place namevectors. A similarity score can be generated by calculating a cosinesimilarity, a Sorensen-Dice coefficient, Jaccard index, or any othersuitable method for calculating the similarity between two vectors.

In step 508, one or more colloquial place names can be identified basedon the plurality of similarity scores. For example, one or morecolloquial place names can be identified as corresponding to one or morecolloquial place name vectors corresponding to a similarity score abovea pre-determined threshold. In some embodiments, a user may determine toidentify a pre-determined number of colloquial place names correspondingto the highest similarity scores (e.g., the colloquial place namescorresponding to the top ten colloquial place name vectors having thehighest similarity scores based on the geographic location information).

FIG. 6 illustrates a flowchart of a method 600 for generating arelational database of colloquial place names according to someembodiments. Method 600 can be performed by a data-processing devicesuch as data-processing device 102 of FIG. 1 . Accordingly, one or moreof the steps below may reference the components of data-processingdevice 102. Method 600 can be performed by processing logic that maycomprise hardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions running on a processingdevice), or a combination thereof.

In step 602, a selection processor (e.g., selection processor 112) ofthe data-processing device receives metadata comprising a plurality ofcolloquial place names. In some embodiments, the metadata may comprise aplurality of colloquial place names corresponding to a plurality ofusers. The colloquial place names may be associated with one or morewords. The users may be users of a social media platform, and two ormore colloquial place names may correspond to each user. In someembodiments, it may be assumed that the two or more colloquial placenames associated with each user are geographically related in ameaningful way.

In step 604, the one or more words associated with a first plurality ofcolloquial place names of the plurality of colloquial place names may beconcatenated to generate a first sequence. This first sequence of one ormore words associated with a first plurality of colloquial place namescan correspond to a first user. A similar process can be performed forstep 606, wherein one or more words corresponding to a second pluralityof colloquial place names of the plurality of colloquial place names isconcatenated to generate a second sequence corresponding to a seconduser.

In step 608, a word-embedding algorithm can be applied to the firstsequence and the second sequence to generate a vector space. The vectorspace can include a vector corresponding to each of the one or morewords corresponding to each colloquial place name of the first andsecond pluralities of colloquial place names. Word-embedding isdescribed in more detail with respect to FIGS. 1 and 2 .

In step 610, the vector space generated in step 608 can be stored into arelational database of colloquial place names.

FIG. 7 illustrates an example of a computer, according to someembodiments. Computer 700 can be a component of a system for identifyingcolloquial place names according to the systems and methods describedabove, such as system 100 of FIG. 1 , or can include the entire systemitself. In some embodiments, computer 700 is configured to execute amethod for identifying one or more colloquial place name based ongeographic location information or generating a relational database,such as methods 500 and 600 of FIGS. 5 and 6 , respectively.

Computer 700 can be a host computer connected to a network. Computer 700can be a client computer or a server. As shown in FIG. 7 , computer 700can be any suitable type of microprocessor-based device, such as apersonal computer, workstation, server, or handheld computing device,such as a phone or tablet. The computer can include, for example, one ormore of processor 710, input device 720, output device 730, storage 740,and communication device 760. Input device 720 and output device 730 cancorrespond to those described above and can either be connectable orintegrated with the computer.

Input device 720 can be any suitable device that provides input, such asa touch screen or monitor, keyboard, mouse, or voice-recognition device.Output device 730 can be any suitable device that provides an output,such as a touch screen, monitor, printer, disk drive, or speaker.

Storage 740 can be any suitable device that provides storage, such as anelectrical, magnetic, or optical memory, including a random accessmemory (RAM), cache, hard drive, CD-ROM drive, tape drive, or removablestorage disk. Communication device 760 can include any suitable devicecapable of transmitting and receiving signals over a network, such as anetwork interface chip or card. The components of the computer can beconnected in any suitable manner, such as via a physical bus orwirelessly. Storage 740 can be a non-transitory computer-readablestorage medium comprising one or more programs, which, when executed byone or more processors, such as processor 710, cause the one or moreprocessors to execute methods described herein, such as methods 500 and600 of FIGS. 5 and 6 , respectively.

Software 750, which can be stored in storage 740 and executed byprocessor 710, can include, for example, the programming that embodiesthe functionality of the present disclosure (e.g., as embodied in thesystems, computers, servers, and/or devices as described above). In someembodiments, software 750 can include a combination of servers such asapplication servers and database servers.

Software 750 can also be stored and/or transported within anycomputer-readable storage medium for use by or in connection with aninstruction execution system, apparatus, or device, such as thosedescribed above, that can fetch and execute instructions associated withthe software from the instruction execution system, apparatus, ordevice. In the context of this disclosure, a computer-readable storagemedium can be any medium, such as storage 740, that can contain or storeprogramming for use by or in connection with an instruction executionsystem, apparatus, or device.

Software 750 can also be propagated within any transport medium for useby or in connection with an instruction execution system, apparatus, ordevice, such as those described above, that can fetch and executeinstructions associated with the software from the instruction executionsystem, apparatus, or device. In the context of this disclosure, atransport medium can be any medium that can communicate, propagate, ortransport programming for use by or in connection with an instructionexecution system, apparatus, or device. The transport-readable mediumcan include but is not limited to, an electronic, magnetic, optical,electromagnetic, or infrared wired or wireless propagation medium.

Computer 700 may be connected to a network, which can be any suitabletype of interconnected communication system. The network can implementany suitable communications protocol and can be secured by any suitablesecurity protocol. The network can comprise network links of anysuitable arrangement that can implement the transmission and receptionof network signals, such as wireless network connections, T1 or T3lines, cable networks, DSL, or telephone lines.

Computer 700 can implement any operating system suitable for operatingon the network. Software 750 can be written in any suitable programminglanguage, such as C, C++, Java, or Python. In various embodiments,application software embodying the functionality of the presentdisclosure can be deployed in different configurations, such as in aclient/server arrangement or through a Web browser as a Web-basedapplication or Web service, for example.

The preceding description sets forth exemplary methods, parameters andthe like. It should be recognized, however, that such description is notintended as a limitation on the scope of the present disclosure but isinstead provided as a description of exemplary embodiments. Theillustrative embodiments described above are not meant to be exhaustiveor to limit the disclosure to the precise forms disclosed. Manymodifications and variations are possible in view of the aboveteachings. The embodiments were chosen and described to best explain theprinciples of the disclosed techniques and their practical applications.Others skilled in the art are thereby enabled to best utilize thetechniques, and various embodiments with various modifications as aresuited to the particular use contemplated.

Although the disclosure and examples have been thoroughly described withreference to the accompanying figures, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims. In the preceding description of the disclosure andembodiments, reference is made to the accompanying drawings, in whichare shown, by way of illustration, specific embodiments that can bepracticed. It is to be understood that other embodiments and examplescan be practiced, and changes can be made without departing from thescope of the present disclosure.

Although the preceding description uses terms first, second, etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother.

Also, it is also to be understood that the singular forms “a,” “an,” and“the” used in the preceding description are intended to include theplural forms as well unless the context indicates otherwise. It is alsoto be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It is further to be understood that the terms“includes, “including,” “comprises,” and/or “comprising,” when usedherein, specify the presence of stated features, integers, steps,operations, elements, components, and/or units but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, units, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

In some embodiments, a non-transitory computer-readable storage mediumstores one or more programs configured to be executed by one or moreprocessors of an electronic device with a display, the one or moreprograms including instructions for implementing any of the stepsdescribed or claimed herein. The present disclosure also relates to adevice for performing the operations herein. This device may bespecially constructed for the required purposes, or it may include ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a non-transitory, computer computer-readable storage medium,such as, but not limited to, any type of disk, including floppy disks,optical disks, CD-ROMs, magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), electrically program read-onlymemories (EPROMs), electronically erasable program read-only memoriesEEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referenced in this disclosure may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The methods, devices, and systems described herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct amore specialized apparatus to perform the required method steps. Therequired structure for a variety of these systems appears from thedescription above. Also, the present disclosure is not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the present disclosure as described herein.

Although the disclosure and examples have been fully described withreference to the accompanying figures, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

1. A method of identifying relationships between colloquial place namesin a relational database comprising: receiving geographic locationinformation; generating a geographic location information vectorcorresponding to the geographic location; comparing the geographiclocation information vector corresponding to the geographic location toa plurality of colloquial place name vectors in a relational databasethat maps a plurality of colloquial place names to a plurality ofcorresponding colloquial place name vectors in a vector space, whereineach colloquial place name vector represents one or more wordsassociated with each colloquial place name, to generate a plurality ofsimilarity scores that is calculated based on the geographic locationinformation vector and each colloquial place name vector of theplurality of colloquial place name vectors; and identifying that one ormore colloquial place names in the relational database are related tothe geographic location information based on the plurality of similarityscores.
 2. The method of claim 1, comprising outputting the one or morecolloquial place names related to the geographic location informationbased on the similarity score onto a display.
 3. The method of claim 1,comprising storing the similarity score calculated based on thegeographic location information vector and each colloquial place namevector of the plurality of colloquial place name vectors in therelational database.
 4. The method of claim 1, comprising updating therelational database based on the geographic location information and thesimilarity score between the geographic location information vector andeach colloquial place name vector of the plurality of colloquial placename vector.
 5. The method of claim 1, wherein each colloquial placename vector of the plurality of colloquial place name vectors isgenerated by word-embedding one or more words associated with acolloquial place name of the plurality of colloquial place names,wherein a first colloquial place name vector represents a firstcolloquial place name and a second colloquial place name vectorrepresents a second colloquial place name.
 6. The method of claim 1,wherein the similarity score is calculated between the geographiclocation information vector and each colloquial place name vector bycalculating a cosine similarity between the geographic locationinformation vector and each colloquial place name vector located in thevector space.
 7. The method of claim 1 comprising: for each user accountof a plurality of user accounts, receiving a plurality of colloquialplace names associated with each user account; and inputting theplurality of colloquial place names associated with each user accountinto a word-embedding algorithm to generate a mapping of the pluralityof colloquial place names to the plurality of corresponding colloquialplace name vectors, wherein the plurality of colloquial place namevectors corresponds to the plurality of colloquial place names.
 8. Themethod of claim 7, wherein the word-embedding algorithm comprises one ofword2vec, GloVe, or FastText.
 9. A system for identifying relationshipsbetween colloquial place names in a relational database comprising: oneor more processors and memory storing one or more programs that whenexecuted by the one or more processors cause the one or more processorsto: receive geographic location information; generate, based on thegeographic location information, a vector corresponding to thegeographic location; compare the geographic location information vectorcorresponding to the geographic location to a plurality of colloquialplace name vectors in a relational database that maps a plurality ofcolloquial place names to a plurality of corresponding colloquial placename vectors in a vector space, wherein each colloquial place namevector represents one or more words associated with each colloquialplace name, to generate a plurality of similarity scores that iscalculated based on the geographic location information vector and eachcolloquial place name vector of the plurality of colloquial place namevectors; and identify that one or more colloquial place names in therelational database are related to the geographic location informationbased on the plurality of similarity scores.
 10. The system of claim 9,comprising output the one or more colloquial place names related to thegeographic location information based on the similarity score onto adisplay.
 11. The system of claim 9, comprising store the similarityscore calculated based on the geographic location information vector andeach colloquial place name vector of the plurality of colloquial placename vectors in the relational database.
 12. The system of claim 9,comprising update the relational database based on the geographiclocation information and the similarity score between the geographiclocation information vector and each colloquial place name vector of theplurality of colloquial place name vector.
 13. The system of claim 9,wherein each colloquial place name vector of the plurality of colloquialplace name vectors is generated by word-embedding one or more wordsassociated with a colloquial place name of the plurality of colloquialplace names, wherein a first colloquial place name vector represents afirst colloquial place name and a second colloquial place name vectorrepresents a second colloquial place name.
 14. The system of claim 9,wherein the similarity score is calculated between the geographiclocation information vector and each colloquial place name vector bycalculating a cosine similarity between the geographic locationinformation vector and each colloquial place name vector located in thevector space.
 15. The system of claim 9 comprising: for each useraccount of a plurality of user accounts, receive a plurality ofcolloquial place names associated with each user account; and input theplurality of colloquial place names associated with each user accountinto a word-embedding algorithm to generate a mapping of the pluralityof colloquial place names to the plurality of corresponding colloquialplace name vectors, wherein the plurality of colloquial place namevectors corresponds to the plurality of colloquial place names.
 16. Thesystem of claim 15, wherein the word-embedding algorithm comprises oneof word2vec, GloVe, or FastText.